Field
Certain aspects of the present disclosure generally relate to neural system engineering and, more particularly, to systems and methods for dynamically assigning and examining synaptic delay.
Background
An artificial neural network, which may comprise an interconnected group of artificial neurons (i.e., neuron models), is a computational device or represents a method to be performed by a computational device. Artificial neural networks may have corresponding structure and/or function in biological neural networks. However, artificial neural networks may provide innovative and useful computational techniques for certain applications in which traditional computational techniques are cumbersome, impractical, or inadequate.
Artificial neural networks are subject to the effects of synaptic delay. Recently, synaptic delay has been used for the emergence of certain network characteristics. During its application, it has been found that synaptic delay may allow for rich features to be implemented. However, the conventional approach to assignment of synaptic delay in network models has been ad hoc at best. In addition, some network models only allow the assignment of synaptic delay during initialization and do not provide any method of dynamically modifying the synaptic delays during simulation. Thus, there is no standard and straightforward method of dynamically assigning and examining the delays for synapses in a network model.